MICHC-NN: An Offline Industrial Control Anomaly Detection Algorithm Based on Maximum Information Coefficient

The industrial control anomaly detection algorithm effectively monitors and identifies anomalies in industrial control systems, ensuring timely issue detection to maintain system reliability, safety, and regular operation. Researchers often integrate a large number of high-precision, high-sampling-frequency sensors into the system to obtain comprehensive real-time equipment operating data. However, deploying consumer-grade high-performance computing devices in large-scale industrial control systems is often impractical, rendering existing anomaly detection systems unusable in practical environments. Most existing sensor feature selection algorithms are unsuitable for industrial control time series datasets, and some suffer from issues such as a limited reduction in the number of sensors and a decrease in anomaly detection accuracy after reduction. We propose an offline industrial control anomaly detection algorithm based on the Maximum Information Coefficient (MICHC-NN) to address these challenges. We utilize the Maximum Information Coefficient to compute correlations among sensors, addressing the issue of existing feature selection algorithms relying on anomaly labels. Additionally, we introduce a key sensor selection algorithm based on correlation, identifying interrelated sensors in an industrial control system and determining the key sensors most susceptible to system anomalies. These key sensors allow the anomaly detection algorithm to be deployed with a smaller footprint in real industrial control environments. Finally, we employ an autoencoder-decoder model with attention mechanisms to train the anomaly detection network. Experimental results demonstrate that our proposed anomaly detection algorithm achieves performance comparable to using all sensors, with a 75% reduction in the number of sensors employed.